#!/usr/bin/env bash
set -xeuo pipefail
# 0. download the config
# only need to download the configuration_deepseek.py and config.json
# remove the `quantization_config` in the `config.json`
# set `num_nextn_predict_layers=0` to disable MTP, which is not currently supported
# huggingface-cli download deepseek-ai/DeepSeek-V3-0324 configuration_deepseek.py config.json

project_name='DAPO'
exp_name='DAPO-qwen3-235b-megatron'
RUNTIME_ENV=verl/trainer/mc2_env.yaml

adv_estimator=grpo

use_kl_in_reward=False
kl_coef=0.0
use_kl_loss=False
kl_loss_coef=0.0

clip_ratio_low=0.2
clip_ratio_high=0.28

enable_filter_groups=True
max_num_gen_batches=30
filter_groups_metric=acc
max_prompt_length=$((1024 * 1))
max_response_length=$((1024 * 34))
enable_overlong_buffer=True
overlong_buffer_len=$((1024 * 4))
overlong_penalty_factor=1.0

loss_agg_mode="token-mean"

train_prompt_bsz=256 # must be > n_gpus. need to fix
gen_prompt_bsz=$((train_prompt_bsz * 1))
n_resp_per_prompt=16
train_prompt_mini_bsz=32 # mini_bsz * n >= micro_bsz * pp * dp

NNODES=${NNODES:-16}

MODEL_PATH="Qwen3-235B-A22B-Thinking-2507"
# python scripts/converter_hf_to_mcore.py --hf_model_path ./Qwen3-235B-A22B-Thinking-2507 --output_path ./Qwen3-235B-A22B-Thinking-2507-Mcore --use_cpu_initialization  -> mcore weight
MCORE_MODEL_PATH="Qwen3-235B-A22B-Thinking-2507 with mcore weight"
CKPTS_DIR=".ckpt"
TRAIN_FILE="BytedTsinghua-SIA/DAPO-Math-17k"
TEST_FILE="BytedTsinghua-SIA/DAPO-Math-17k"

# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7

# Performance Related Parameter
use_dynamic_bsz=True
# actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
# infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3))
offload=True

gen_tp=4
gen_dp=32
rollout_max_num_seqs=2048
max_num_batched_tokens=$((1024 * 2))
train_tp=4
train_ep=8
train_pp=8
train_cp=8

actor_ppo_max_token_len=$((max_prompt_length + max_response_length))
infer_ppo_max_token_len=$((max_prompt_length + max_response_length))
actor_ppo_max_token_len=$((actor_ppo_max_token_len / train_cp))
infer_ppo_max_token_len=$((infer_ppo_max_token_len / train_cp))

python3 -m recipe.dapo.main_dapo --config-path=../../verl/trainer/config --config-name='ppo_megatron_trainer' \
    data.train_files="${TRAIN_FILE}" \
    data.val_files="${TEST_FILE}" \
    data.prompt_key=prompt \
    data.truncation='left' \
    data.max_prompt_length=${max_prompt_length} \
    data.max_response_length=${max_response_length} \
    data.train_batch_size=${train_prompt_bsz} \
    +data.gen_batch_size=${gen_prompt_bsz} \
    actor_rollout_ref.rollout.n=${n_resp_per_prompt} \
    algorithm.adv_estimator=${adv_estimator} \
    algorithm.use_kl_in_reward=${use_kl_in_reward} \
    algorithm.kl_ctrl.kl_coef=${kl_coef} \
    actor_rollout_ref.actor.use_kl_loss=${use_kl_loss} \
    actor_rollout_ref.actor.kl_loss_coef=${kl_loss_coef} \
    actor_rollout_ref.actor.clip_ratio_low=${clip_ratio_low} \
    actor_rollout_ref.actor.clip_ratio_high=${clip_ratio_high} \
    actor_rollout_ref.actor.clip_ratio_c=10.0 \
    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=32 \
    +algorithm.filter_groups.enable=${enable_filter_groups} \
    +algorithm.filter_groups.max_num_gen_batches=${max_num_gen_batches} \
    +algorithm.filter_groups.metric=${filter_groups_metric} \
    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=32 \
    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \
    actor_rollout_ref.model.path="${MODEL_PATH}" \
    actor_rollout_ref.actor.optim.lr=1e-6 \
    actor_rollout_ref.actor.optim.weight_decay=0.1 \
    actor_rollout_ref.actor.ppo_mini_batch_size=${train_prompt_mini_bsz} \
    actor_rollout_ref.actor.megatron.param_offload=${offload} \
    actor_rollout_ref.actor.megatron.optimizer_offload=${offload} \
    actor_rollout_ref.actor.megatron.grad_offload=${offload} \
    actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=${train_pp} \
    actor_rollout_ref.actor.megatron.tensor_model_parallel_size=${train_tp} \
    actor_rollout_ref.actor.megatron.expert_model_parallel_size=${train_ep} \
    actor_rollout_ref.actor.megatron.context_parallel_size=${train_cp} \
    +actor_rollout_ref.actor.megatron.override_transformer_config.context_parallel_size=${train_cp} \
    actor_rollout_ref.actor.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \
    actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \
    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \
    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \
    +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \
    +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage=11 \
    +actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=11 \
    actor_rollout_ref.actor.entropy_coeff=0 \
    actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
    actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
    actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
    +actor_rollout_ref.model.use_remove_padding=True \
    actor_rollout_ref.actor.loss_agg_mode=${loss_agg_mode} \
    actor_rollout_ref.rollout.gpu_memory_utilization=0.6 \
    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
    +actor_rollout_ref.rollout.dp_model_parallel_size=${gen_dp} \
    +actor_rollout_ref.rollout.enable_expert_parallel=True \
    actor_rollout_ref.rollout.load_format="safetensors" \
    actor_rollout_ref.rollout.enable_chunked_prefill=False \
    actor_rollout_ref.rollout.max_num_batched_tokens=${max_num_batched_tokens} \
    actor_rollout_ref.rollout.max_model_len=$((max_prompt_length + max_response_length)) \
    actor_rollout_ref.rollout.max_num_seqs=${rollout_max_num_seqs} \
    actor_rollout_ref.rollout.temperature=${temperature} \
    actor_rollout_ref.rollout.top_p=${top_p} \
    actor_rollout_ref.rollout.top_k=${top_k} \
    actor_rollout_ref.rollout.val_kwargs.temperature=${temperature} \
    actor_rollout_ref.rollout.val_kwargs.top_p=${val_top_p} \
    actor_rollout_ref.rollout.val_kwargs.top_k=${top_k} \
    actor_rollout_ref.rollout.val_kwargs.do_sample=True \
    actor_rollout_ref.rollout.val_kwargs.n=1 \
    actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=${train_pp} \
    actor_rollout_ref.ref.megatron.tensor_model_parallel_size=${train_tp} \
    actor_rollout_ref.ref.megatron.expert_model_parallel_size=${train_ep} \
    actor_rollout_ref.ref.megatron.param_offload=${offload} \
    actor_rollout_ref.ref.megatron.dist_checkpointing_path=${MCORE_MODEL_PATH} \
    reward_model.reward_manager=dapo \
    +reward_model.overlong_buffer.enable=${enable_overlong_buffer} \
    +reward_model.overlong_buffer.len=${overlong_buffer_len} \
    +reward_model.overlong_buffer.penalty_factor=${overlong_penalty_factor} \
    +reward_model.overlong_buffer.log=False \
    +reward_model.max_resp_len=${max_response_length} \
    trainer.logger=['console'] \
    trainer.project_name="${project_name}" \
    trainer.experiment_name="${exp_name}" \
    trainer.n_gpus_per_node=16 \
    trainer.nnodes="${NNODES}" \
    trainer.device=npu \
    trainer.val_before_train=False \
    trainer.test_freq=-1 \
    trainer.save_freq=100 \
    trainer.total_epochs=1 \
    trainer.total_training_steps=100 \
    trainer.default_local_dir="${CKPTS_DIR}" \
    trainer.resume_mode=auto \
    trainer.log_val_generations=-1 \
    actor_rollout_ref.rollout.name=vllm \
    actor_rollout_ref.nccl_timeout=7200 \
    actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
    actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
    actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
    +actor_rollout_ref.actor.megatron.override_transformer_config.use_flash_attn=True \
    ++actor_rollout_ref.ref.megatron.override_transformer_config.use_flash_attn=True $@
